#pix2pix

This repository provides PyTorch implementations for both CycleGAN and pix2pix, supporting both unpaired and paired image-to-image translation. Ideal for research and experimentation, the codebase is compatible with the latest PyTorch releases and optimizes training with advanced models like img2img-turbo and contrastive-unpaired-translation. It includes resources such as training tips, FAQs, Docker support, and Colab notebooks for practical usage.

Discover how conditional adversarial networks enable efficient image-to-image translation, converting input images into desired outputs with minimal resources. This method supports quick training on small datasets, achieving satisfactory results in creating visual models like architectural facades. Suitable for various applications, it uses multiple datasets, covering scenarios from urban to natural settings. The PyTorch version provides ongoing enhancements, offering performance that matches or exceeds the initial implementation.